As humans live longer, the occurrence and severity of retinal pathologies such as glaucoma, macular degeneration, macular edema, and the like continues to increase.
Glaucoma is an irreversible progressive optic neuropathy characterized by changes in the parapapillary retinal nerve fiber layer (RNFL) and optic disc. The human eye has about 0.75 to 1.25 million retinal ganglion cells that transmit the visual information from the eye to the brain. At a cellular level, glaucoma is characterized by a progressive death of these cells and their axons by a process of apoptosis that is measured as a progressive thinning of nerve fiber layer and neuroretinal rim tissue of the optic disc. The heterogeneous nature of the disease and redundancy in the visual system makes glaucoma very difficult to identify in early stages of the disease, thus making glaucoma the leading cause of blindness worldwide.
Macular degeneration is a medical condition that results in a lost of vision in the center of the visual field (the “macula”) because of damage of the retina. In the “dry” form, macular degeneration is caused by the accumulation of cellular debris (“drusen”) between the retina and the choroids. In the “wet” form, blood vessels grow up from the choroids behind the retina.
Macular edema is a medical condition that occurs when fluid and protein deposits collect on or under the macula and cause the macula to thicken and swell.
Structural damage, such as RNFL defects, is often observed and precedes functional damage. Medical devices, such as the scanning laser polarimeters (GDx devices), optical coherence tomographs (OCTs), and Heidelberg retina tomographs that measure the RNFL, may aid in early diagnosis of retinal pathologies such as glaucoma.
The current analysis is usually limited to the mean of RNFL at different locations around the parapillary retina at a given distance from the optic disc as a function of angle. Such thickness graph, also known as temporal, superior, nasal, inferior, temporal (TSNIT) graph of thickness for a ring around the retina, shows a general double-hump pattern of thickness due to the much greater number of ganglion cell axons entering the disc superiorly and inferiorly. Although the mean RNFL can discriminate groups of glaucomatous individuals from ocular healthy individuals, classification performance can be quite limited when using mean thickness for classification.
Due to this issue, the TSNIT features have been mathematically characterized and it is proven that shape-analysis methods like Fast Fourier Analysis (FFA) and Wavelet-Fourier Analysis (WFA) have better classification performances in differentiating between glaucomatous eyes and the healthy eyes.
Fast Fourier analysis (FFA) linearly breaks up the features into sinusoidal curves (i.e., into a set of sinusoids in which each sinusoid is a different scale, or frequency) and thus has a different number of humps across the TSNIT data set. Wavelet-Fourier analysis adopts a discrete wavelet transform (DWT), which is more suitable for analyzing discontinuities and abrupt changes contained in signals.
However, both FFA and WFA techniques yield only marginal improvements over standard methods. Accordingly, there is a need for systems, methods, and computer-readable media for detecting glaucoma and other retinal pathologies that outperform both currently technology and FFA and WFA analysis.
Likewise, detection of glaucomatous progression is critical in monitoring glaucoma patients and preventing irreversible vision loss. Although measuring visual field loss through standard automated perimetry (SAP) has been widely used in diagnosing glaucomatous progression, it has been shown that structural changes in the retinal nerve fiber layer may precede functional changes obtained by SAP. Glaucomatous progression is also known to be difficult to differentiate from test variability. Accordingly, there remains a need for systems, methods, and computer-readable media for predicting a progression of retinal pathologies such as glaucoma.